Merge branch 'feature_wuch' into 'master'

fix graph converter

See merge request !434

mace/kernels/conv_2d.h

@@ -399,10 +399,10 @@ struct Conv2dFunctor<DeviceType::CPU, float> : Conv2dFunctorBase {
       transformed_output.Resize(transformed_output_shape);
       const float *transformed_filter_ptr;
       if (transformed_filter_.dim_size() == 0) {
-        transformed_filter_.Resize(transformed_filter_shape);
         if (is_filter_transformed_) {
           transformed_filter_ptr = filter_data;
         } else {
+          transformed_filter_.Resize(transformed_filter_shape);
           switch (winograd_out_tile_size) {
             case 2:
               TransformFilter4x4(filter_data,

mace/python/tools/caffe_converter_lib.py

@@ -303,7 +303,7 @@ class CaffeConverter(object):
         arg.i = self.dt
         return output_name
 
-    def add_input_transform(self, names):
+    def add_gpu_input_transform(self, names):
         for name in names:
             new_input_name = MACE_INPUT_NODE_NAME + '_' + name + ":0"
             op_def = self.net_def.op.add()
@@ -327,7 +327,7 @@ class CaffeConverter(object):
                 output_shape = input_op.output_shape_map[input_op.name]
             self.add_output_shape(op_def, output_shape)
 
-    def add_output_transform(self, names):
+    def add_gpu_output_transform(self, names):
         for name in names:
             output_name = MACE_OUTPUT_NODE_NAME + '_' + name + ":0"
             op_def = self.net_def.op.add()
@@ -420,18 +420,16 @@ class CaffeConverter(object):
             # OIHW -> HWOI
             weight_data = op.data[0].transpose((2, 3, 0, 1))
 
-        if self.device == 'cpu' and use_winograd:
+        if use_winograd:
             self.convert_winograd_conv_filter_cpu(op, op_def)
-        else:
-            self.add_tensor(weight_tensor_name, weight_data)
-
-        if self.device == 'gpu':
+        elif self.device == 'gpu':
             buffer_type = "DW_CONV2D_FILTER" \
                 if is_depthwise else "CONV2D_FILTER"
             output_name = self.add_buffer_to_image(weight_tensor_name,
                                                    buffer_type)
             op_def.input.extend([output_name])
         else:
+            self.add_tensor(weight_tensor_name, weight_data)
             op_def.input.extend([weight_tensor_name])
 
         # Add Bias
@@ -1111,14 +1109,16 @@ class CaffeConverter(object):
                 output_shape = input_op.output_shape_map[input_op.layer.top[0]]
             else:
                 output_shape = input_op.output_shape_map[input_op.name]
-            self.add_output_shape(op_def, output_shape)
+            self.add_output_shape(op_def,
+                                  [output_shape[0], output_shape[2],
+                                   output_shape[3], output_shape[1]])
 
     def convert(self, input_nodes, input_shapes, output_nodes):
         assert self.ops[0].type == 'Input'
         self.add_input_op_shape(input_nodes, input_shapes)
 
         if self.device == 'gpu':
-            self.add_input_transform(input_nodes)
+            self.add_gpu_input_transform(input_nodes)
 
         if self.device == 'cpu':
             self.add_cpu_input_transform(input_nodes)
@@ -1164,7 +1164,7 @@ class CaffeConverter(object):
                                                               op.type))
 
         if self.device == 'gpu':
-            self.add_output_transform(output_nodes)
+            self.add_gpu_output_transform(output_nodes)
 
         if self.device == 'cpu':
             self.add_cpu_output_transform(output_nodes)

mace/python/tools/tf_converter_lib.py

@@ -202,20 +202,39 @@ class TFConverter(object):
             dims_arg.name = 'dims'
             dims_arg.ints.extend([0, 2, 3, 1])
 
-            self.add_output_shape(self.ops[name].outputs, op_def)
-
-    @staticmethod
-    def add_output_shape(outputs, op):
+            output_shapes = []
+            for output in self.ops[name].outputs:
+                old_shape = output.shape.as_list()
+                # NCHW -> NHWC
+                if len(old_shape) == 2:
+                    new_shape = [old_shape[0], 1, 1, old_shape[1]]
+                else:
+                    new_shape = [old_shape[0], old_shape[2],
+                                 old_shape[3], old_shape[1]]
+                output_shape = mace_pb2.OutputShape()
+                output_shape.dims.extend(new_shape)
+                output_shapes.append(output_shape)
+            op_def.output_shape.extend(output_shapes)
+
+    def add_output_shape(self, outputs, op):
         output_shapes = []
         for output in outputs:
-            output_shape = mace_pb2.OutputShape()
+            old_shape = []
             if isinstance(output, list):
-                output_shape.dims.extend(output)
+                old_shape = output
             elif isinstance(output, tf.Tensor):
                 if output.shape.num_elements() is not None:
-                    output_shape.dims.extend(output.shape.as_list())
+                    old_shape = output.shape.as_list()
             else:
                 raise ValueError('output type not supported: ', type(output))
+            if len(old_shape) == 2:
+                old_shape = [old_shape[0], old_shape[1], 1, 1]
+
+            if self.device == 'cpu':  # NHWC -> NCHW
+                old_shape = [old_shape[0], old_shape[3],
+                             old_shape[1], old_shape[2]]
+            output_shape = mace_pb2.OutputShape()
+            output_shape.dims.extend(old_shape)
             output_shapes.append(output_shape)
         op.output_shape.extend(output_shapes)
 
@@ -1089,15 +1108,6 @@ class TFConverter(object):
         self.add_output_shape(op.outputs, op_def)
         self.resolved_ops[op.name] = 1
 
-    def replace_in_out_name(self, input_names, output_names):
-        in_names = set([input_name + ":0" for input_name in input_names])
-        out_names = set([output_name + ":0" for output_name in output_names])
-        for op in self.net_def.op:
-            if op.input[0] in in_names:
-                op.input[0] = MACE_INPUT_NODE_NAME + '_' + op.input[0]
-            if op.output[0] in out_names:
-                op.output[0] = MACE_OUTPUT_NODE_NAME + '_' + op.output[0]
-
     def convert(self, input_nodes, output_nodes):
         if self.device == 'gpu':
             self.add_gpu_input_transform(input_nodes)